CN110440830B - Self-alignment method of vehicle-mounted strapdown inertial navigation system under movable base - Google Patents
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Abstract
The invention discloses a self-alignment method of a vehicle-mounted strapdown inertial navigation system under a movable base, which comprises the following steps of: step 1, obtaining geographical position information of a strapdown inertial navigation system, wherein the geographical position information comprises longitude lambda and latitude
Acceleration of gravity g; step 2, using longitude, latitude, gravity acceleration information and inertial device output information, adopting a solidification coordinate system, and using a multi-vector attitude determination method to perform rough alignment to obtain an initial attitude matrix; step 3, constructing a kalman filtering state equation and a measurement equation according to the strapdown inertial system error equation and the inertial device error equation; and 4, designing a hybrid calibration Kalman filtering method, estimating the system error quantity and the inertial device error quantity, correcting, realizing a fine alignment process, and finishing initial alignment. The invention has stronger anti-interference capability and ensures the self-alignment precision under the condition of a movable base.
Description
Technical Field
The invention particularly relates to a self-alignment method of a vehicle-mounted strapdown inertial navigation system under a movable base, and relates to establishment of a Kalman filtering state space model based on solidification coordinate system coarse alignment and fine alignment under the movable base and a Kalman filtering method based on a hybrid calibration method. The self-alignment method is suitable for self-alignment processes of other carriers with the same working state under vehicle-mounted environments such as vehicle body shaking, up and down of personnel, natural interference, vehicle engine starting and the like.
Background
For a strapdown inertial navigation system (strapdown inertial navigation system), initial alignment is an important prerequisite for its navigation task, and the purpose is to establish an accurate initial coordinate reference, i.e. a posture matrix of a carrier system relative to a navigation system. The alignment accuracy and the alignment speed are two important technical indicators of the initial alignment. The precision of initial alignment has an important influence on the navigation precision of the inertial navigation system, and the speed of initial alignment determines the quick response capability of the weapon system to a great extent, so that the requirements on high alignment precision, short alignment time, precision and quickness are met.
Initial alignment the initial alignment of the inertial navigation system can be classified according to different division criteria as follows: according to the degree of dependence on external information, the method can be divided into active alignment and non-active alignment, wherein matching parameters can be provided according to whether higher-precision main inertial navigation is needed or not, and the method can be divided into transfer alignment and self-alignment; the alignment stage can be generally divided into coarse alignment and fine alignment; the alignment can be divided into static base alignment and moving base alignment according to the moving state of the base.
A common self-alignment scheme is that under the condition of ignoring disturbance, an accelerometer and a gyroscope are used for measuring two vectors which are not collinear in space to realize coarse alignment, a coarse attitude matrix is obtained, and then a Kalman filtering algorithm based on a small-misalignment-angle linear error model is used for finishing accurate estimation of an initial attitude misalignment angle. However, when the carrier is in a shaking interference condition, the output of the inertia measurement assembly comprises interference acceleration information and interference angular velocity information, and in view of the fact that the signal-to-noise ratio of a gyro output signal is low and the frequency band of an interference signal is wide, it is difficult to obtain accurate earth rotation angular velocity, so that the coarse alignment precision is not high, even an effective alignment result cannot be obtained, and the influence on the fine alignment result is large.
In the fine alignment process, the Kalman filtering is commonly used for estimating the system state variables, and the accurate attitude transformation matrix is solved to realize the fine alignment process. However, in a practical system, due to the influence of dynamic interference conditions in the measurement process, the linear approximation degree of the system model is weakened, so that the filtering effect is deteriorated, and even the filter divergence can be directly caused. For the filtering environment, it is necessary to provide a new filtering method, which can correct the state error in the system, ensure the stability of filtering for a long time, and implement a precise alignment process with high precision.
Disclosure of Invention
The invention aims to provide a self-alignment method of a vehicle-mounted strapdown inertial navigation system under a movable base aiming at the condition that the system noise is increased under the condition of the movable base, firstly, a solidification method is adopted for rough alignment, on the basis that the time can be accurately known, the rotation angle of a gravity acceleration vector in an inertial space is calculated, and true north information contained in the change of the gravity acceleration vector in the direction of the inertial space is extracted to realize the rough alignment; then on the basis of finishing the rough alignment, constructing a kalman filtering state equation and a measurement equation by utilizing an error equation of an inertial navigation system and an error equation of an inertial device; and finally, estimating the speed error, the position error, the attitude error, the inertial device error and the like of the inertial navigation system by using a hybrid calibration Kalman filtering method, and correcting the speed error, the position error, the attitude error, the inertial device error and the like to realize high-precision self-alignment. The invention has stronger anti-interference capability and ensures the self-alignment precision under the condition of a movable base.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a self-alignment method of a vehicle-mounted strapdown inertial navigation system under a movable base is characterized by comprising the following steps:
step 1, obtaining geographical position information of a strapdown inertial navigation system, wherein the geographical position information comprises longitude lambda and latitude
Acceleration of gravity g;
step 2, using longitude, latitude, gravity acceleration information and inertial device output information, adopting a solidification coordinate system, and using a multi-vector attitude determination method to perform rough alignment to obtain an initial attitude matrix;
step 3, constructing a kalman filtering state equation and a measurement equation according to the strapdown inertial system error equation and the inertial device error equation;
and 4, designing a hybrid calibration Kalman filtering method, estimating the system error quantity and the inertial device error quantity, correcting, realizing a fine alignment process, and finishing initial alignment.
As a preferable mode, in the step 2, the initial attitude matrix
Decomposition is of the form in the following equation (1):
wherein,
As a preferred mode of execution,
wherein,
is an initial unit array, is selected>
Is output by the gyroscope>
Represents the projection of the carrier motion angular speed of the carrier coordinate system b relative to the geocentric inertial coordinate system i in the carrier coordinate system b, and/or>
Is a vector->
A constructed cross-product antisymmetric matrix;
Selecting the velocities at m time instants as reference vectors, and constructing an objective function in formula (5):
wherein,
in the formula (7), f b Is the accelerometer output;
obtaining by arranging a deformation formula (5):
L(C)＝L'-tr(CB T ) (8)
wherein,
and is independent of B;
is an orthogonal matrix C that minimizes L;
and D, performing singular value decomposition on the B to obtain:
B＝USV T (9)
in formula (9), U and V are orthogonal matrices; s = diag (S) 1 ,s 2 ,s 3 )，s 1 ≥s 2 ≥s 3 ；
Obtaining:
as a preferable mode, in step 3, the strapdown inertial system error equation and the inertial device error equation are as follows:
wherein, δ v e 、δv n 、δv u Respectively determining east-direction, north-direction and sky-direction speed errors of the inertial navigation system; phi is a unit of e 、φ n 、φ u Pitch, roll, azimuth error, respectively;
zero offset of the X, Y and Z triaxial accelerometers respectively; tau is a Zero-offset correlation time for the accelerometer; epsilon is zero drift of the gyro; tau. g The zero drift correlation time of the gyroscope; omega ie The rotational angular velocity of the earth; r M Radius of latitude circle of the earth, R N Is the longitude circle radius of the earth; w is a a And w g White noise of zero mean values for the accelerometer and gyroscope, respectively;
according to an error equation of a strapdown inertial navigation system, establishing a system state equation as follows:
wherein A (t) is a state transition matrix obtained by formula (11); the scale coefficient error and the installation error of the inertial device are omitted, and because the influence of the repeatability error of the bias of the inertial device on the system precision is the largest, the random constant parts of the gyro drift and the acceleration zero offset are listed into a state, and the filtering state vector and the system noise are respectively as follows:
assuming that the position of the carrier is unchanged and is accurately known, the position error is selected as an observed quantity, and then the measurement equation is:
wherein,
respectively resolving longitude and latitude values for the strapdown inertial navigation system; v is white noise, H is measurement matrix, H = [ I = 2×2 0 2×12 ]。
Discretizing the expression (12) and the expression (15) to obtain a system discretized kalman filtering state equation and a measurement equation.
As a preferable mode, the step 4 includes a step 41 and a step 42, wherein:
step 41, designing a hybrid calibration kalman filtering method:
the output calibration uses the kalman filter equation in equation (16):
wherein,
representing a one-step prediction estimation value;
Represents X k A state estimate of (a); k k A gain matrix for time k; p k,k-1 Representing a one-step prediction estimation variance matrix; p is k-1 Representing a state estimation variance matrix; r k Representing a measurement noise variance matrix; q k-1 Representing a system noise variance matrix; the system state equation and the measurement equation with feedback control are:
the corresponding filter equation is:
will control the vector U k-1 The selection is as follows:
the filter equation for the feedback calibration is:
step 42, correcting error and attitude error in the fine alignment process:
the attitude strapdown matrix is modified as follows:
wherein,
kalman filtering estimates the misalignment angle phi e 、φ n 、φ u Then, the attitude matrix is calibrated by a hybrid calibration method
And (5) calibrating to accurately determine the posture of the carrier and finish the self-alignment process.
The invention has the following beneficial effects:
1. introducing an inertia solidification idea: on the basis that the time can be accurately known, the rotational angular velocity of the earth can be accurately calculated. The rotation angle of the gravity acceleration vector in the inertial space is calculated by selecting a plurality of intermediate moments, and the true north information contained in the change of the gravity acceleration vector in the direction of the inertial space is extracted to realize coarse alignment. The method only utilizes the movement of the angular velocity of rotation and the gravity acceleration vector of the earth in the inertial space, avoids the influence of interference information on the measurement of an inertial device when the carrier is positioned on a movable base, and effectively improves the coarse alignment precision.
2. The invention provides a hybrid calibration kalman filtering method, namely, output calibration is firstly carried out at the initial stage of filtering, and feedback calibration is carried out after filtering is stable. When feedback calibration is carried out, the estimation error quantity is fed back to the system, so that the divergence of a filtering result can be prevented, the filtering convergence speed is higher, and the self-alignment precision is higher.
3. Feedback calibration is carried out after filtering is stable, the updated state covariance matrix Pk reflects the stable state of filtering estimation, the eight diagonal elements are taken as reference quantity for judging filtering stability, the reference quantity is compared with a set value, if the reference quantity is smaller than the set value, the filtering is considered to be in the stable state at the moment, system errors and inertial device errors can be corrected, corrected error parameters are directly calculated in the next period, and the errors can be gradually reduced until the errors disappear after the process is repeated.
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FIG. 1 is a flow chart of a self-alignment process of a strap-down inertial navigation system under a moving base.
Fig. 2 is a flow chart of the hybrid calibration kalman filtering method.
Detailed Description
The following detailed description of the specific implementation steps of the present invention is made with reference to fig. 1 and 2:
the coordinate system is defined as follows in the detailed description of the implementation steps below:
global coordinate system e is as follows: the origin is the center of the earth, the X axis is located in the equatorial plane and points to the meridian where the carrier is located from the center of the earth, the Z axis rotates along with the earth rotation axis along the direction of the earth rotation axis, and the X axis, the Y axis and the Z axis form a right-hand coordinate system and rotate along with the earth rotation.
The geocentric inertial coordinate system i is as follows: the inertial coordinate system refers to a reference coordinate system which is static in space or moves linearly at a uniform speed. The origin of the earth center inertial coordinate system is taken at the earth center O e Right hand rectangular inertial frame of time. z is a radical of formula i The axis pointing along the earth's axis to the north pole, y i Axis, z i The axis is in the equatorial plane of the earth and points towards the two stars in space.
Navigation coordinate system n system: is a coordinate system selected as a navigation reference according to the operation requirement of a navigation system during navigation. According to the invention, a northeast sky coordinate system is selected as a navigation coordinate system, namely Ox points to the horizontal east direction (e), oy points to the horizontal north direction (n), and Oz points to the sky (u) along the vertical direction.
The carrier coordinate system b is as follows: the origin of coordinates O is located at the center of gravity of the carrier, the longitudinal axis Oy b Pointing forwards along the head and tail lines, transverse axis Ox b To the right of it, oz b Perpendicular to Ox b y b Pointing up along the vertical axis of the carrier.
Inertial coagulation coordinate system i b0 Comprises the following steps: at t 0 Obtaining a carrier coordinate system b after inertial solidification at any moment, wherein t 0 Is the starting time of the coarse alignment.
The self-alignment method of the vehicle-mounted strapdown inertial navigation system under the movable base comprises the following steps:
step 1, obtaining geographical position information of a strapdown inertial navigation system, wherein the geographical position information comprises longitude lambda and latitude
Acceleration of gravity g.
And 2, carrying out coarse alignment by using longitude, latitude, gravity acceleration information and inertial device output information and a solidification coordinate system and using a multi-vector attitude determination method to obtain an initial attitude matrix.
Initial attitude matrix
Decomposition is of the form in the following equation (1):
wherein,
wherein,
is an initial unit array, is selected>
For gyroscope output, in conjunction with a reference signal>
Represents the projection of the carrier motion angular speed of the carrier coordinate system b relative to the geocentric inertial coordinate system i in the carrier coordinate system b, and/or>
Is a vector->
A cross-product antisymmetric matrix of construction.
wherein,
in the formula (7), f b Is the accelerometer output;
obtaining by arranging a deformation formula (5):
L(C)＝L'-tr(CB T ) (8)
wherein,
and is independent of B;
is an orthogonal matrix C (determinant 1) in which L is the minimum value.
And D, performing singular value decomposition on the B to obtain:
B＝USV T (9)
in formula (9), U and V are orthogonal matrices; s = diag (S) 1 ,s 2 ,s 3 )，s 1 ≥s 2 ≥s 3 ；
Obtaining:
v at time t is calculated from equation (6) i (t) calculating time t from equation (7)
Then the formula (10) can calculate->
Based on the respective expressions (2) to (4) < u > based on >>
And (3) substituting the formula (1) to obtain a rough initial attitude matrix to finish rough alignment.
And 3, constructing a kalman filtering state equation and a measurement equation according to the strapdown inertial system error equation and the inertial device error equation. The strapdown inertial system error equation and the inertial device error equation are as follows:
wherein, δ v e 、δv n 、δv u Respectively determining east-direction, north-direction and sky-direction speed errors of the inertial navigation system; phi is a e 、φ n 、φ u Pitch, roll, azimuth error, respectively;
zero offset of the X-axis accelerometer, the Y-axis accelerometer and the Z-axis accelerometer respectively; tau is a Zero offset correlation time for the accelerometer; epsilon is zero drift of the gyro; tau is g The zero drift correlation time of the gyroscope; omega ie The rotational angular velocity of the earth; r is M Radius of latitude circle of the earth, R N Is the longitude circle radius of the earth; w is a a And w g Respectively, white noise with zero mean value of the accelerometer and the gyroscope.
According to an error equation of a strapdown inertial navigation system, establishing a system state equation as follows:
wherein, A (t) is a state transition matrix obtained by formula (11); scale coefficient errors and installation errors of the inertial device are omitted, and because the repeatability errors of the bias of the inertial device have the largest influence on the precision of the system, random constant parts of gyro drift and acceleration zero offset are listed into states, and then filtering state vectors and system noise are respectively as follows:
assuming that the carrier position is unchanged and is precisely known, the position error is selected as the observed quantity, and then the measurement equation is:
wherein,
respectively resolving longitude and latitude values for the strapdown inertial navigation system; v is white noise, H is measurement matrix, H = [ I = 2×2 0 2×12 ]。
Discretizing the expression (12) and the expression (15) to obtain a system discretized kalman filtering state equation and a measurement equation.
And 4, designing a hybrid calibration kalman filtering method to estimate the system error amount and the inertial device error amount and correct, so as to realize a fine alignment process and complete initial alignment. The specific implementation method comprises the following steps:
(1) Design hybrid calibration kalman filtering method
At present, two common methods for calibrating a system by applying a kalman filtering method are: output calibration and feedback calibration. When output calibration is carried out, the product carries out filtering estimation but the estimated value does not correct the system state quantity; when the feedback calibration is applied, the filter estimation of the error value of the navigation parameter output by the navigation system is fed back to the internal calculation and directly participates in each navigation calculation, and under the ideal condition, the error amount can be gradually eliminated in the continuous iteration process. However, a certain time is required from the beginning to the stabilization of the kalman filter, and when the filtering is just started, the estimation error is large, and studies show that the accuracy of the filtering estimation is influenced by the motion state of the carrier, so if the navigation error with the large estimation error is compensated into the internal calculation of the system by adopting feedback calibration at the moment, the accuracy of the navigation system is more easily reduced, and therefore, the feedback calibration cannot be performed in real time when the filtering is unstable in general. Based on the analysis of the applicability of the two, the invention uses an output calibration mode to carry out filtering estimation at the filtering starting stage, carries out feedback calibration after filtering is stable, namely corrects the system speed error, the attitude error, the position error and the inertial device error by using the filtering estimation value, directly participates in the calculation of the next period after the corrected system error, and repeats the process until the self-alignment is finished.
The filtering stable state is obtained by estimating a variance matrix P through taking the state k The eight diagonal elements reflect the stability of the corresponding estimated value, and compare the stability with a set value (set according to an actual system and experimental conditions), if the stability is smaller than the set value, the filter is stable, at the moment, the filter estimated value corrects the corresponding system error of the state variable and the error of an inertial device, and the corrected error parameter participates in the calculation of the next period again, otherwise, only the output calibration is performed.
The output calibration can be realized by adopting a general kalman filter equation in formula (16):
wherein,
representing a one-step prediction estimation value;
Represents X k A state estimate of (a); k is k A gain matrix for time k; p k,k-1 Representing a one-step prediction estimation variance matrix; p k-1 Representing a state estimation variance matrix; r k Representing a measurement noise variance matrix; q k-1 Representing the system noise variance matrix.
The system state equation and the measurement equation with feedback control are:
compared with the basic filter equation, the feedback calibration only adds the control item B in the state equation k-1 U k-1 In which B is k-1 Is a control vector U k-1 The coefficient matrix of (2). The corresponding filter equation is:
according to the principle of feedback calibration, the effect of calibration is to reduce the estimated error amount until there is no estimation error, so that the control vector U can be adjusted k-1 The selection is as follows:
the filter equation for the feedback calibration is:
(2) Correction of error attitude in fine alignment process
The attitude matrix determined by the coarse alignment is an initial value of the attitude update of the fine alignment process. Because the error of the attitude matrix determined by the coarse alignment is larger, a deviation angle exists between the navigation coordinate system n' actually established and the ideal navigation coordinate system n required to be established, and the deviation angle is an alignment misalignment angle. Setting the misalignment angle to phi e 、φ n 、φ u After the coarse alignment, the deviation angles are all small angles, so φ = [ φ = e φ n φ u ] T Can be regarded as equivalent rotation vectors from n to n'. The attitude strapdown matrix is modified as follows:
wherein,
from equation (23), the kalman filter estimates the misalignment angle φ e 、φ n 、φ u Then, the attitude matrix is calibrated by a hybrid calibration method
And (5) calibrating to accurately determine the posture of the carrier and finish the self-alignment process.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (4)
1. A self-alignment method of a vehicle-mounted strapdown inertial navigation system under a movable base is characterized by comprising the following steps:
step 1, obtaining geographical position information of a strapdown inertial navigation system, wherein the geographical position information comprises longitude lambda and latitude
Acceleration of gravity g;
step 2, using longitude, latitude, gravity acceleration information and inertial device output information, adopting a solidification coordinate system, and using a multi-vector attitude determination method to carry out coarse alignment to obtain an initial attitude matrix;
step 3, constructing a kalman filtering state equation and a measurement equation according to the strapdown inertial system error equation and the inertial device error equation;
step 4, designing a hybrid calibration kalman filtering method to estimate the error amount of the system and the error amount of the inertial device and correct the error amount to realize a fine alignment process and complete initial alignment; the step 4 includes a step 41 and a step 42, in which:
step 41, designing a hybrid calibration kalman filtering method:
the output calibration uses the kalman filter equation in equation (16):
wherein,
representing a one-step predictive estimate;
Represents X k A state estimate of (a); k k A gain matrix at time k; p k,k-1 Representing a one-step prediction estimation variance matrix; p is k-1 Representing a state estimation variance matrix; r k Representing a measurement noise variance matrix; q k-1 Representing a system noise variance matrix;
the system state equation and the metrology equation with feedback control are:
the corresponding filter equation is:
will control the vector U k-1 The selection is as follows:
the filter equation for the feedback calibration is:
step 42, correcting error and attitude error in the fine alignment process:
the attitude strapdown matrix is modified as follows:
wherein,
kalman filtering estimates the misalignment angle phi e 、φ n 、φ u Then, the attitude matrix is calibrated by a hybrid calibration method
And (5) calibrating, accurately determining the posture of the carrier, and finishing the self-alignment process.
2. The self-alignment method of the strap-down inertial navigation system under the moving base of claim 1, wherein in the step 2, the initial attitude matrix
Decomposition is of the form in the following equation (1):
wherein,
3. The self-alignment method of the strap-down inertial navigation system under a moving base according to claim 2,
wherein,
records a carrier coordinate system b and a carrier inertial solidification coordinate system i b0 The information of the attitude change between the systems,
represents the projection of the carrier motion angular speed of the carrier coordinate system b relative to the geocentric inertial coordinate system i in the carrier coordinate system b, and/or>
Is a vector>
A constructed cross-product antisymmetric matrix;
selecting the velocities at m time instants as reference vectors, and constructing an objective function in formula (5):
wherein,
in the formula (7), f b Is the accelerometer output;
obtaining by arranging a deformation formula (5):
L(C)＝L'-tr(CB T ) (8)
wherein,
and is independent of B;
Is an orthogonal matrix C that minimizes L;
and D, performing singular value decomposition on the B to obtain:
B＝USV T (9)
in formula (9), U and V are orthogonal matrices; s = diag (S) 1 ,s 2 ,s 3 )，s 1 ≥s 2 ≥s 3 ；
Obtaining:
4. the self-alignment method of the strapdown inertial navigation system under the moving base according to claim 1 or 2, wherein in the step 3, the error equation of the strapdown inertial system and the error equation of the inertial device are as follows:
wherein, δ v e 、δv n 、δv u Respectively determining east-direction, north-direction and sky-direction speed errors of the inertial navigation system; phi is a e 、φ n 、φ u Pitch, roll, azimuth errors, respectively; tau is a Zero offset correlation time for the accelerometer; epsilon is zero drift of the gyroscope; tau is g The zero drift correlation time of the gyroscope; omega ie Is the rotational angular velocity of the earth; r M Radius of latitude circle of the earth, R N Is the radius of the earth longitude circle; w is a a And w g White noise of zero mean values for the accelerometer and gyroscope, respectively;
according to an error equation of the strapdown inertial navigation system, establishing a system state equation as follows:
wherein A (t) is a state transition matrix obtained by formula (11); the scale coefficient error and the installation error of the inertial device are omitted, and because the influence of the repeatability error of the bias of the inertial device on the system precision is the largest, the random constant parts of the gyro drift and the acceleration zero offset are listed into a state, and the filtering state vector and the system noise are respectively as follows:
assuming that the carrier position is unchanged and is precisely known, the position error is selected as the observed quantity, and then the measurement equation is:
wherein,
respectively resolving longitude and latitude values for the strapdown inertial navigation system; v is white noise measurement, H is measurement matrix, H = [ I = 2×2 0 2×12 ]；
Discretizing the formula (12) and the formula (15) to obtain a system discretized kalman filtering state equation and a measurement equation.
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| CN111795696A (en) * | 2020-06-28 | 2020-10-20 | 中铁第一勘察设计院集团有限公司 | Initial structure optimization method of multi-inertial navigation redundancy system based on zero-speed correction |
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| CN106871928B (en) * | 2017-01-18 | 2020-09-25 | 北京工业大学 | Strap-down inertial navigation initial alignment method based on lie group filtering |
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